Gen AI Developer with LLM & Chatbot

Tech Mahindra - Austin, TX

Full-time

Austin, TX

Professional, Scientific, and Technical Services

About the position

The Gen AI Developer with a focus on Large Language Models (LLMs) and chatbots is responsible for designing, developing, and deploying advanced chatbots and virtual assistants. This role requires a deep understanding of LLMs such as GPT and BERT, as well as the ability to create and optimize prompts to enhance model performance and response accuracy. The developer will implement reinforcement learning techniques to enable adaptive learning and improve decision-making capabilities within LLM-based systems. In addition to chatbot development, the role involves applying data classification algorithms to categorize and structure large volumes of text data, ensuring accurate data processing and analysis. The developer will also design and implement workflows that integrate LLMs into various business processes, thereby enhancing automation and efficiency. Utilizing advanced Natural Language Processing (NLP) techniques and machine learning algorithms is essential to improve the functionality and capabilities of LLM-based applications. Collaboration is key in this position, as the developer will work closely with cross-functional teams, including data scientists, product managers, and software engineers, to deliver high-quality AI-driven solutions. Rigorous testing, validation, and evaluation of LLM-based models will be conducted to ensure their accuracy, reliability, and scalability. Comprehensive documentation of models, processes, and workflows will be maintained to ensure transparency and facilitate knowledge transfer. Continuous learning is also a critical aspect of this role, as the developer must stay updated on the latest advancements in LLMs, NLP, and AI, applying this knowledge to drive innovation within the organization.

Responsibilities

• Design, develop, and deploy sophisticated chatbots and virtual assistants using Large Language Models (LLMs) like GPT, BERT, or similar frameworks.
• Create, optimize, and fine-tune prompts to achieve desired outcomes in LLM-based applications, enhancing model performance and response accuracy.
• Implement reinforcement learning techniques to improve LLM-based systems, enabling adaptive learning and better decision-making.
• Develop and apply data classification algorithms using LLMs to categorize and structure large volumes of text data, ensuring accurate data processing and analysis.
• Design and implement workflows that integrate LLMs into various business processes, enhancing automation and efficiency.
• Utilize advanced NLP techniques and machine learning algorithms to improve the functionality and capabilities of LLM-based applications.
• Work closely with cross-functional teams, including data scientists, product managers, and software engineers, to deliver high-quality AI-driven solutions.
• Conduct rigorous testing, validation, and evaluation of LLM-based models to ensure accuracy, reliability, and scalability.
• Maintain comprehensive documentation of models, processes, and workflows, ensuring transparency and ease of knowledge transfer.
• Stay updated on the latest advancements in LLMs, NLP, and AI, and apply this knowledge to continuously improve and innovate within the role.

Requirements

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Proven experience in developing chatbots and conversational AI using LLMs.
• Strong expertise in prompt engineering and optimization for LLM-based applications.
• Experience with reinforcement learning techniques and their application in LLMs.
• Proficiency in data classification methods using LLMs and NLP techniques.
• Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or similar.
• Excellent problem-solving skills and the ability to work independently or as part of a team.
• Strong programming skills in Python, JavaScript, or other relevant languages.
• Ability to communicate complex technical concepts effectively to both technical and non-technical stakeholders.